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approaches to couple machine learning potentials with molecular mechanics force fields, and they will investigate the use of machine learning potentials as data sources for training force fields. They will
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develop methodologies (such as acoustic emission method) detecting early signs of damage, leaks, or degradation before they become critical. We will also leverage the latest developments in machine learning
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approach that integrates machine learning algorithms, blockchain technology, and IoT devices with digital twin systems. The scientific objectives of the project are as follows: Objective 1: Investigate how
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for Real-World Optimisation and AI Applications Brain-Computer Interfaces & their Applications Computational Neuroscience: Reinforcement Learning and Microzones in the Cerebellum Explainable Generative
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Start Date: Between 1 August 2026 and 1 July 2027 Introduction: This project fuses machine learning (ML) based inverse design approaches and topology optimisation (TO) to realise multiscale
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the state-of-the-art wind tunnel facilities of the Department of Aeronautics, and will utilize novel theoretical and machine learning tools. You can expect to become an expert in aerodynamics and turbulent
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Subject area: Drug Discovery, Sustainability, Laboratory Automation, Microfluidics, Machine Learning Overview: This highly interdisciplinary 4-year funded PhD studentship will contribute to cutting
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the many good reasons to want to research and teach at the University of Vienna, there is one in particular, which has convinced around 7,500 academic staff members so far. They see themselves as
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the University of Liverpool. As well as gaining a formal HE teaching qualification, you’ll learn key pedagogical skills for the academic job market, primarily through shadowing mentored classroom involvement
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certification, dramatically accelerating innovation cycles. What you will gain: Expertise in Finite Element Analysis, Scientific Machine Learning, Uncertainty Quantification, and Professional Programming